Mapping Cyclist Safety in New York City

Is there a relationship between bike path access and cyclist injuries?

I was inspired by this CityLab article detailing the impending transportation crisis due to the MTA L Line shutdown. Specifically, I am interested in observing whether the shutdown contributes to an increase in cyclist injuries due to motor vehicle collisions as about 275,000 L Line riders seek alternatives.

NYC DOT has predicted about 2% of L Line riders to switch to cycling, while about 79% are predicted to move to other subway lines. I could investigate the dispersion of L Train riders to other options as a whole if data is available, but for now I will focus on cyclist safety as it’s more approachable with currently available data.

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Let’s start by looking at some performance metrics for the MTA subway network as a whole.

Some of the performance metric definitions are opaque, so I’ll specify where necessary.

Now let’s look at some two-wheeled data.

And now a map of cyclist injuries and deaths over the last few years.

Series: ts_injuries 
ARIMA(0,1,3)(0,1,1)[52] 

Coefficients:
          ma1     ma2      ma3     sma1
      -0.6902  0.0121  -0.2195  -0.5122
s.e.   0.0831  0.1001   0.0821   0.1416

sigma^2 estimated as 337.8:  log likelihood=-651.65
AIC=1313.3   AICc=1313.71   BIC=1328.31

Training set error measures:
                    ME     RMSE      MAE       MPE     MAPE      MASE
Training set -1.589831 15.57204 10.44011 -4.812162 13.84195 0.5500583
                    ACF1
Training set -0.01016123

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Planned Updates / Notes:

This is an ongoing project that I am doing in my free time. If you have any constructive criticism please feel free to reach out, especially with suggestions about spatial regression methodology.

• Methodology
  • Need a way to do geospatial predictive modeling with a dataset that only has 1s, no 0s
  • How to understand relationship between accidents when we only have accident data, not non-accidents
  • Consider using Citibike data as a proxy to cyclist population, but do they behave differently?
  • Look into spatial prediction methods (spatial autocorrelation, spatial regression, etc.)
  • What trends are there in types of bike paths being constructed over time?
• Time-Series
  • More rigorous analysis of cyclist injuries
  • Use ARIMA model to remove seasonality, conclude whether injuries are increasing
  • Are injuries increasing because more people are biking or something else?
    
• Geospatial
  • Consider changing map to chloropleths (and use accidents per capita)
  • Find a way to visualize changes over time, whether through leaflet or gganimate or otherwise
  • Need function that can calculate a point’s (accident) nearest polyline (bike path) and return features (type of path)
    
• Visualizations
  • Change drag behavior (for mobile) when plotly is updated to 1.43